David Lindell


I'm a fifth-year Ph.D. student at Stanford University in the Computational Imaging Lab. My work is at the intersection of optimization, machine learning, optics, and hardware. Along these lines I've been developing next-generation computational LIDAR systems and algorithms for imaging around corners. I'm generally interested in problems in 3D imaging, inverse scattering, optimization, and computer vision, with a goal of developing computational methods to push the boundaries of current imaging capabilities. My work is relevant to a broad range of applications including autonomous vehicle navigation, medical imaging, remote sensing, and robotic vision.


Sep 2020

Our paper on Sinusoidal Representation Networks (SIREN) was accepted as an oral to NeurIPS (1% acceptance rate).

Sep 2020

My paper on imaging through scattering media was published in Nature Communications and featured in Stanford News.

Aug 2020

My thesis presentation “Computational Single-Photon Imaging” received the honorable mention award at the SIGGRAPH Thesis Fast Forward!

Aug 2020

My course on Computational time-resolved imaging, single-photon sensing, and non-line-of-sight imaging is live at SIGGRAPH! I’m joined by excellent instructors Matthew O’Toole, Ramesh Raskar, and Srinivas Narasimhan.

July 2020

July 2020

I was recognized as an outstanding reviewer for CVPR 2020! (136/3663 reviewers selected)

Jun 2020

May 2020

I’m co-chairing the 9th annual Computational Cameras and Displays workshop at CVPR 2020 with Achuta Kadambi and Katie Bouman.

Mar 2020

Update: My talk is featured on the TED website with nearly a quarter million views!

Jan 2020

My TedxBeaconStreet talk on “a camera to see around corners” is up on YouTube!

May 2019

Two papers accepted! Acoustic Non-Line-of-Sight Imaging was accepted as an oral to CVPR, and Wave-Based Non-Line-of-Sight Imaging Using Fast f-k Migration was accepted to SIGGRAPH.

Jun 2018

I’m interning at the Intelligent Systems Lab at Intel this summer with Vladlen Koltun.

Mar 2018

Our paper on Seeing around corners was published in Nature!


Implicit neural representations with periodic activation functions

Vincent Sitzmann, Julien N.P. Martel, Alexander Bergman, David B. Lindell, Gordon Wetzstein

NeurIPS 2020 (oral)

An implicitly defined neural signal representation for images, audio, shapes, and wavefields.

Three-dimensional imaging through scattering media based on confocal diffuse tomography

David B. Lindell, Gordon Wetzstein

Nature Communications

A new computationally efficient method to recover the 3D shape of objects hidden behind a thick scattering layer.

3D imaging with an RGB camera and a single SPAD transient

Mark Nishimura, David B. Lindell, Christopher Metzler, Gordon Wetzstein

ECCV 2020

Adding minimal additional hardware significantly improves monocular depth estimation using a single RGB camera.

Non-line-of-sight surface reconstruction using the directional light-cone transform

Sean I. Young, David B. Lindell, Bernd Girod, David Taubman, Gordon Wetzstein

CVPR 2020 (oral)

A joint albedo–normal approach to non-line-of-sight (NLOS) surface reconstruction using the directional light-cone transform (D-LCT).

Deep adaptive LIDAR: End-to-end optimization of sampling and depth completion at low sampling rates

Alexander W. Bergman, David B. Lindell, Gordon Wetzstein

ICCP 2020

An end-to-end differentiable depth imaging system which jointly optimizes the LiDAR scanning pattern and sparse depth inpainting.

SPADnet: Deep RGB-SPAD sensor fusion assisted by monocular depth estimation

Zhanghao Sun, David B. Lindell, Olav Solgaard, Gordon Wetzstein

Optics Express (2020)

A neural sensor fusion framework for robust 3D imaging with single-photon detectors.

Wave-based non-line-of-sight imaging using fast f–k migration

David B. Lindell, Matthew O'Toole, Gordon Wetzstein


A robust, wave-based image formation model for the problem of non-line-of-sight (NLOS) imaging.

Non-line-of-sight imaging with partial occluders and surface normals

Felix Heide, Matthew O"Toole, Kai Zang, David B. Lindell, Steven Diamond, Gordon Wetzstein

ACM Transactions on Graphics 2019

A new approach to non-line-of-sight imaging that estimates partial occlusions in the hidden volume along with surface normals.

Acoustic non-line-of-sight imaging

David B. Lindell, Gordon Wetzstein, Vladlen Koltun

CVPR 2019 (oral)

A novel approach to seeing around corners using acoustic echoes.

Sub-picosecond photon-efficient 3D imaging using single-photon sensors

Felix Heide, Steven Diamond, David B. Lindell, Gordon Wetzstein

Scientific Reports (2018)

State-of-the-art depth estimation with pileup correction for single-photon avalanche diodes.

Single-photon 3D imaging with deep sensor fusion

David B. Lindell, Matthew O'Toole, Gordon Wetzstein


Capturing 3D geometry with only single photons captured photons per scan position using deep neural networks.

Towards transient imaging at interactive rates with single-photon detectors

David B. Lindell, Matthew O'Toole, Gordon Wetzstein

ICCP 2018

Capturing and reconstructing transient images with single-photon avalanche diodes (SPAD) at interactive rates.

Confocal non-line-of-sight imaging based on the light-cone transform

Matthew O'Toole, David B. Lindell, Gordon Wetzstein

Nature (2018)

A confocal scanning technique solves the reconstruction problem of non-line-of-sight imaging to give fast and high-quality reconstructions of hidden objects.

Reconstructing transient images from single-photon sensors

Matthew O'Toole, Felix Heide, David B. Lindell, Kai Zang, Steven Diamond, Gordon Wetzstein

CVPR 2017 (Spotlight)

Capturing light transport at billions of frames per second using ultra-sensitive photo-detectors.

High-resolution soil moisture retrieval with ASCAT

David B.Lindell and David Long

IEEE Geoscience and Remote Sensing Letters (2016)

Estimation of soil moisture using satellite-based radar.

Multiyear Arctic ice classification using ASCAT and SSMIS

David B. Lindell and David Long

Remote Sensing (2016)

Classification of first-year and multiyear ice in the Arctic using active radar and passive radiometers.

Multiyear Arctic ice classification using OSCAT and QuikSCAT

David B. Lindell and David Long

IEEE Transactions on Geoscience and Remote Sensing (2015)

A technique to classify first-year and multiyear ice in the Arctic based on radar backscatter.


  • 2016 – Present

    Stanford University

    Ph.D. Electrical Engineering

  • 2015 – 2016

    Brigham Young University

    M.S. Electrical Engineering

  • 2009 – 2015

    Brigham Young University

    B.S. Electrical Engineering

Invited Talks

Nov 2019

TEDxBeaconStreet 2019

A camera to see around corners

Nov 2019

Boston University Center for Information & Systems Engineering

Computational imaging with single-photon detectors

Nov 2019

MIT Research Laboratory of Electronics

Efficient confocal non-line-of-sight imaging

Sep 2019

Berkeley Center for Computational Imaging

Computational imaging with single-photon detectors

May 2019

Silicon Valley ACM SIGGRAPH Chapter

Computational single-photon imaging

May 2019

Stanford Center for Image Systems Engineering

Computational imaging with single-photon detectors

Jan 2019

Carnegie Mellon University Graphics Lab

Computational single-photon imaging